Knowledge that Transforms

Using account-level credit card data from a large Turkish bank, we study the impact of a unique credit card policy that increases minimum payment on consumption and debt repayment. We show that the policy reduces credit card spending and debt, boosts existing debt repayment, and reduces credit card delinquency. The credit card debt of affected consumers falls on average by 50% two years into the policy’s implementation. An increase in minimum payment has a stronger effect than does a decrease of a similar magnitude. We build a benchmark life cycle model with soft liquidity constraint to explain the reduction in credit card spending.

The interplay between investors’ demand and providers’ incentives has shaped the evolution of exchange-traded funds (ETFs). While early ETFs invested in broad-based indexes and therefore offered diversification at low cost, more recent products track niche portfolios and charge high fees. Strikingly, over their first 5 years, specialized ETFs lose about 30% (risk-adjusted). This underperformance cannot be explained by high fees or hedging demand. Rather, it is driven by the overvaluation of the underlying stocks at the time of the launch. Our results are consistent with providers catering to investors’ extrapolative beliefs by issuing specialized ETFs that track attention-grabbing themes.

Between March and August 2020, S&P and Moody’s downgraded approximately 25% of collateral feeding into CLOs and only 2% of tranche values, with rating actions concentrating in junior tranches. Both S&P and Moody’s modeling indicate that the impacts should have been considerably larger, especially for higher-rated tranches. Neither changes in correlation nor the accumulation of pre-COVID-19 protective cushions can explain the downgrade asymmetry on upper tranches. Instead, CLO managers repositioned their collateral pools to dampen the negative credit shock and rating agencies incorporated qualitative adjustments in their CLO ratings. Important potential policy and market implications from these findings are discussed.

We find evidence of selective exposure to confirmatory information among 400,000 users on the investor social network StockTwits. Self-described bulls are five times more likely to follow a user with a bullish view of the same stock than are self-described bears. Consequently, bulls see 62 more bullish messages and 24 fewer bearish messages than bears do over the same 50-day period. These “echo chambers” exist even among professional investors and are strongest for investors who trade on their beliefs. Finally, beliefs formed in echo chambers are associated with lower ex post returns, more siloing of information, and more trading volume.

Big data technologies change the way in which data and labor combine to create knowledge. Is this a modest innovation or a data revolution? Using hiring and wage data, we estimate firms’ data stocks and their knowledge production functions. Quantifying changes in production functions informs us about the likely long-run changes in output, in factor shares, and in the distribution of income, due to big data technologies. For the investment management industry, our structural estimates predict a 5% decline in the labor share of income; that change is comparable to similar estimates for the industrial revolution.

Through a survey and analyses of observational data, we provide systematic evidence that institutional investors value and demand climate risk disclosures. The survey reveals the investors have a strong demand for climate risk disclosures, and many actively engage their portfolio firms for improvements. Empirical analyses of holdings data corroborate this evidence by showing a significantly positive association between climate-conscious institutional ownership and better firm-level climate risk disclosure. We establish further evidence of institutional investors’ influence on firms’ climate risk disclosures by examining a shock to the climate risk disclosure demand of French institutional investors (French Article 173).

We compare the investment of standalone firms across regions after a positive shock to the investment opportunities generated by a large-scale highway development project. We show that the standalones’ investment sensitivity is lower in regions with a higher density of business groups in the local area. We investigate mechanisms driving our results and find support for a financing mechanism whereby banks allocate capital preferentially to group-affiliated firms in responding to the increase in credit demand. Overall, our study documents that business groups have spillover effects on standalone firms.

We document that the aggregate hiring rate of publicly traded firms in the U.S. economy negatively predicts stock market returns and long-term cash flows, and positively predicts short-term cash flows. In addition, through a variance decomposition, we show that the time-series variation in the aggregate hiring rate is mainly driven by changes in discount rates and short-term expected cash flows, with no contribution from variation in long-term expected cash flows. We estimate a neoclassical dynamic model with labor market frictions and show that labor adjustment costs and time-varying risk are essential for the model to replicate the empirical patterns.

We study the design of stress tests that provide information about aggregate and idiosyncratic risk in banks’ portfolios and impose contingent capital requirements. In the optimal static test, an adverse scenario fails all weak and some strong banks, limiting the stigma of failure. Sequential tests outperform static tests. Under natural conditions, the optimal sequential test consists of a precautionary recapitalization, followed by a scenario that fails only weak banks, similar to TARP in 2008, followed by SCAP in 2009. Our results also shed light on the Federal Reserve’s decision to test the banks twice in 2020 during the COVID-19 pandemic.

We examine innovation following the Great Depression using data on a century’s worth of U.S. patents and a difference-in-differences design that exploits regional variation in the crisis severity. Harder-hit areas experienced large and persistent declines in independent patenting, mostly reflecting the disruption in access to finance during the crisis. This decline was larger for young and inexperienced inventors and lower-quality patents. In contrast, innovation by large firms increased, especially among young and inexperienced inventors. Overall, the Great Depression contributed to the decline in technological entrepreneurship and accelerated the shift of innovation into larger firms.